A D-vine copula-based model for repeated measurements extending linear mixed models with homogeneous correlation structure

Matthias Killiches, Claudia Czado

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We propose a model for unbalanced longitudinal data, where the univariate margins can be selected arbitrarily and the dependence structure is described with the help of a D-vine copula. We show that our approach is an extremely flexible extension of the widely used linear mixed model if the correlation is homogeneous over the considered individuals. As an alternative to joint maximum–likelihood a sequential estimation approach for the D-vine copula is provided and validated in a simulation study. The model can handle missing values without being forced to discard data. Since conditional distributions are known analytically, we easily make predictions for future events. For model selection, we adjust the Bayesian information criterion to our situation. In an application to heart surgery data our model performs clearly better than competing linear mixed models.

Original languageEnglish
Pages (from-to)997-1005
Number of pages9
JournalBiometrics
Volume74
Issue number3
DOIs
StatePublished - Sep 2018

Keywords

  • Linear mixed models
  • Longitudinal data
  • Repeated measurements
  • Unbalanced setting
  • Vine copulas

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